Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models

作者：

Highlights：

• A new hybrid method to integrate deep neural networks with multiple financial time series models is proposed.

• Combines the LSTM model with various generalized autoregressive conditional heteroskedasticity (GARCH)-type models.

• Compared performance of the proposed hybrid LSTM models with that of existing methodologies.

摘要

•A new hybrid method to integrate deep neural networks with multiple financial time series models is proposed.•Combines the LSTM model with various generalized autoregressive conditional heteroskedasticity (GARCH)-type models.•Compared performance of the proposed hybrid LSTM models with that of existing methodologies.

论文关键词：LSTM,GARCH,Deep learning,Volatility prediction,Hybrid model

论文评审过程：Received 25 November 2017, Revised 10 February 2018, Accepted 1 March 2018, Available online 6 March 2018, Version of Record 12 March 2018.

论文官网地址：https://doi.org/10.1016/j.eswa.2018.03.002